Unexpected equipment failures can be expensive and potentially catastrophic, resulting in unplanned production downtime, costly replacement of parts and safety and environmental concerns. With many factories and process control plants facing an ever-increasing shortage of experienced personnel, many are now looking for AI based systems to replace the ‘experienced old guy’ who knows everything about the machine and reduce their Total Cost of Ownership (TOC).

The challenge is however, how do you build and train an AI CbM system to replace an expert ?

What is CbM?

As part of the I4.0 revolution, Condition based monitoring (CbM) of machines has received a great amount of attention, as factories look to maximise their production efficiency and reduce their TOC, while at the same time retaining the invaluable skills of experienced foremen and production workers. As such, CbM is a process for monitoring equipment during operation to identify any deterioration, enabling maintenance to be planned and operational costs reduced.

CbM 5G edge computing

Many are factory owners are suspicious of cloud-based enterprise solutions offered by Microsoft, Amazon and Google as data leaves the site and any latency issues could affect production output. Recently 5G edge computing has received much attention, whereby all time-critical operations are undertaken at the edge (i.e. near to the asset in the factory) via smart sensors.

Arm’s rich set of Cortex processors offer a combination of high performance, ML/DSP computation support and low power. This is further strengthened by Arm’s new Helium Cortex-M55 and Cortex-M85 AI based processors that have been specially designed for edge-based AI applications – the latter offers an impressive 3DMIPS/MHz making it a good fit for ML and DSP algorithms. These processors and supporting libraries now allow developers to develop high-performance CbM smart sensors to perform their computationally intensive tasks at the edge and communicate the results via a 5G network to a smartphone or database. This provides higher reliability and scalability than expensive cloud-based solutions reliant on big data.

It would seem that big data has had its day!

Vibration sensor technology

Contactless MEMS (microelectromechanical systems) accelerometers sensors are an excellent alternative to the well-established, but bulky and expensive (25-500+ EUR) Piezo sensors for obtaining vibration information. MEMS sensors are relatively low cost (10-30 EUR) and can offer a response down to DC (zero Hertz), which is useful for the detection of imbalance at very low rotational speeds. MEMS accelerometers also have a self-test feature whereby the sensor can be verified to be 100% functional. They produce acceleration data that can be analysed by various vibration monitoring algorithms.

Spectral vibration monitoring via the FFT (Fast Fourier Transform) is regarded as an industry standard for machine vibration analysis. If a mechanical problem exists, the FFT spectra (multiple spectrums) will provide information to help determine the source and cause of the problem. Coupled with the right AI algorithms, the features from the FFT analysis can be used to identify the root cause of the failure, such as motor imbalance, misalignment, and looseness. These properties and challenges faced by the FFT will be discussed further later on in the article.

There are several steps to follow as guidelines to help achieve a successful vibration monitoring programme. The following is a general list of these steps:

  • Collect useful information: Look, listen and feel the machinery to check for resonance. Identify what measurements are needed (point and point type). Conduct additional testing if further data are required.
  • Analyse spectral data: Evaluate the overall values and specific frequencies corresponding to machinery anomalies. Compare overall values in different directions and current measurements with historical data.
  • Multi-parameter monitoring: Use additional techniques to conclude the fault type. (Analysis tools such as phase measurements, current analysis, acceleration enveloping, oil analysis and thermography can also be used.)
  • Perform Root Cause Analysis (RCA): In order to identify the real causes of the problem and to prevent it from occurring again.
  • Reporting and planning actions: Use a Computer Maintenance Management System (CMMS) to rectify the problem and take action to achieve a plan.

Getting acceleration, velocity and/or displacement estimates

As aforementioned, a popular device used to obtain acceleration data is a so-called ‘accelerometer’. These devices are semiconductor-based MEMS (microelectromechanical systems) and provide 3D (i.e. tri-axial) acceleration time domain data to a supporting microcontroller.

Before FFT analysis, the accelerometer data is usually passed through integration signal processing blocks, in order to convert the time domain acceleration data into velocity and displacement data. These blocks are comprised of a highpass filter and cumulative sum (integration). The highpass filter is essential for removing the effects of DC and noise, which would cause an offset in the output (i.e. the result of the integration). Depending on the severity of the noise/DC the output may even saturate, making it unusable for analysis. The design of a suitable highpass filter is an extremely challenging task and is the primary reason why many vibration analysis systems struggle to measure vibrations <10Hz (600 RPM).

Collect useful information

When conducting a vibration program, certain preliminary information is needed in order to conduct an analysis. The identification of components, running speed, operating environment and types of measurements should be determined initially to assess the overall system.

Identify components of the machine that could cause vibration

Before a spectrum can be analysed, the components that cause vibration within the machine must be identified. For example, you should be familiar with these key components:

  • If the machine is connected to a fan or pump, it is important to know the number of fan blades or impellers.
  • If bearings are present, know the bearing identification number or its designation.
  • If the machine contains, or is coupled, to a gearbox, know the number of teeth and shaft speeds.
  • If the machine is driven with belts, know the belt lengths.

The above information helps assess spectral components and helps identify the vibration source. Determining the running speed is the initial task. There are several methods to help identify this parameter.

Identifying the running speed

Knowing the machine’s running speed is critical when analysing an FFT spectrum. Running speed is related to most components within the machine and therefore, aids in assessing overall machine health. There are several ways to determine running speed:

  • Read the speed from instrumentation at the machine or from instrumentation in the control room monitoring the machine.
  • Look for peaks in the spectrum at 1,800 or 3,600 RPM (60Hz countries), 1,500 and 3,000 RPM (50Hz countries) if the machine is an induction electric motor, as electric motors usually run at these speeds. If the machine is variable speed, look for peaks in the spectrum that are close to the running speed of the machine during the time at which the data is captured.
  • An FFT’s running speed peak is typically the first significant peak in the spectrum when reading the spectrum from left to right. Search for this peak and check for peaks at two times, three times, four times, etc. (at the harmonic frequencies).

Challenges with the FFT algorithm

FFT spectra allow us to analyse vibration amplitudes at various component frequencies on the FFT spectrum. In this way, we can identify and track vibration occurring at specific frequencies. Since we know that particular machinery problems generate vibration at specific frequencies, we can use this information to diagnose the cause of excessive vibration.

Challenges with spectral analysis

  • The sampling rate of the accelerometer drifts with temperature: This results in a mismatch between the FFT analysis sampling frequency and the real situation. As such, the amplitude and frequency estimates of the vibration will be incorrect.
  • Frequency resolution: the frequency of the vibration peak may have a fractional value. If the resolution of the Fourier algorithm is not fine enough, it will ‘smear’ the result, resulting in a lower amplitude estimate.
  • Running speed: this is typically known apriori, but will have a degree of error associated with it and will change with temperature. For example, 3000 rpm ±1% is 50Hz ±0.5Hz at the fundamental running frequency. In order to track higher harmonics (i.e. multiples of the running speed) the FFT must have sufficient frequency resolution to accurately estimate the amplitude at the right frequency.

Traditional FFT based analysis uses a very high number of computational points in order to achieve a 1Hz resolution. Although this is OK, it still does not overcome the fractional frequency components and requires considerable computational effort.

Some designs use a phaselocked loop, that tracks the running frequency and sets the FFT analysis sampling frequency to a multiple (e.g. 20x) of the running speed. Although this is a very good workaround, it requires specialised hardware (such as an expensive ASIC) and is inflexible for changes in running speed.

ML feature extraction, DSP algorithms and models

In order to build an ML (machine learning) model for an AI CbM application, several challenges need to be overcome.

  • Definition of classes: In order to make a classification, ML classes must be defined. In the simplest sense, this can be Fault or Normal behaviour, but what about other cases?
  • ML Features: what data features will be used for the ML model? Running speed, harmonics, RMS amplitude? What physical and mathematical principles should I use to build these algorithms?
  • Obtaining ML training data: How will you obtain suitable datasets for ML training? In many cases this is not easy to obtain, as many foremen will not allow any disruption to their time-critical production lines.
  • Preparing datasets: After answering the aforementioned questions, the next challenge will be to capture and prepare the datasets for the ML classification. This is traditionally where a good 90% of a data scientist’s time will be spent. Therefore, it is prudent to invest in high fidelity feature extraction edge algorithms in order to expedite this step. This will also have the advantage of increasing the reproducibility and consistency of the results, which is where many AI based systems perform poorly.

ASN’s IP blocks and applications

ASN’s vibration IP blocks combine the Fourier transform’s time-frequency integration property, data filtering and a specialised high frequency resolution tracking algorithm to implement the ARAHTA (adaptive running speed and harmonics tracking) algorithm. ARAHTA tracks the vibration sensor’s ODR (output data rate) and calculates the motor/pumps running speed using the sensor’s accelerometer sensor data in real-time. ARAHTA’s high resolution and adaptive tracking mechanism results in a typical running speed accuracy of ±1 RPM across the temperature range and sub-mm displacement accuracy using noisy accelerometer data.

ARAHTA’s high accuracy and flexibility ensures that the resulting ML features are high quality and very consistent in the presence of temperature change and load shifts. This has a significant advantage for CbM applications, whereby fingerprinting a spectral profile can be used to assess the degradation of assets of interest. ARAHTA’s high-resolution spectrum forms the basis of providing an AI algorithm with high accuracy feature-rich information, suitable for classification.

Algorithmic performance

A comparison of the FFT vs the ASN ARAHTA IP blocks is shown below. Setting up a test accelerometer signal comprised of an 8.2Hz sinusoid with amplitude 1g and a few harmonic frequencies at various amplitudes, we can objectively compare the methods.

Analysing Figure 1, notice that the plot shows a comparison of the acceleration spectrum (i.e. the FFT of the acceleration data, shown in red) and the displacement spectrum, shown in blue. Analysing the first peak, notice that as the FFT’s resolution is insufficient, as the algorithm has identified the peak at 8.75Hz, rather than at 8.2Hz. This has a consequence for the amplitude estimation, as the acceleration spectrum amplitude is around 0.34g, rather than the expected 1g. As such, the algorithm incorrectly estimates the displacement at 8.2Hz to be 1mm, rather than 3.69mm.

The true value can be seen in Figure 2, where ARAHTA correctly finds the first resonant peak at 8.2Hz and estimates the correct amplitude of 3.69mm.

Figure 1 – Displacement estimate via FFT (frequency resolution: 813.5mHz):
wrong frequency and amplitude estimation
Figure 2 – Displacement estimate via ARAHTA (frequency resolution: 10mHz):
correct amplitude and frequency estimation.

Get in touch and reduce your asset’s TCO

ASN contactless measurement sensor technology and smart algorithms are an ideal solution for AI based CbM applications. Please contact our CbM expert team to see how we can help you create an effective maintenance programme and reduce your asset’s Total Cost of Ownership.

Author

  • Dr. Sanjeev Sarpal

    Sanjeev is an AIoT visionary and expert in signals and systems with a track record of successfully developing over 25 commercial products. He is an Arm Ambassador and advises top international blue chip companies on their AIoT solutions and strategies for I4.0, telemedicine, smart healthcare, smart grids and smart buildings.

“Improve your existing resources”

In a previous blog, we talked about how IoT can help in taking control. There is another step further: to optimize your processes with AIOT.

Benefits

  • Better use of existing resources
  • Take the right decisions at the right time
  • Optimal circumstances

Better use of existing resources

Control means you have a clear overview how assets are being used. Such as:

  • How long does each step in a process take?
  • What are the whereabouts of my assets (trucks, cranes, forklifts, containers…)?
  • What is the state of maintenance?

The next step is of course, to optimize your business.

First of all, many blogs write about total-new situations. In fact, most AIOT is needed in companies which are already established. With their inventory, processes, customers and all the responsibilities which come with them. Large investments have been made to reach the business today. And so, their processes may not be optimal, at least they work. So how to benefit from AIOT, without throwing away all these investments? And: how to be sure processes are at least working as they do know? Smart sensors help to bring the whole process at today’s level, without throwing away resources which are working fine. Besides, companies can choose to implement AIOT piecemeal.

This is especially the case when it’s about highly essential functions such as infrastructure, sluices and installations. Here, the asset is not just an asset, but a part of a total infrastructure. Downtime of such an asset has large implications for society as a whole.

Many processes are still monitored piecemeal. A further optimization is to connect systems with each other. Get 1 overview in 1 dashboard. Learn how your processes are doing, and where are the optimizations are required.

Take the right decisions at the right time

To measure is to know, to know is to be able to improve.

One most mentioned benefits of AIOT is preventive maintenance. Preventive maintenance means that something is repaired or replace, before it is breaks. Or at least, to maintain while the damage is still small. In normal situations there would be downtime, now repairs can be made scheduled. And if downtime is needed for repairs, then it can be scheduled at times the least inconvenient.

It’s already been said: to be able to schedule repairs. Take the right decisions at the right time. Besides, in the old situation, a foreman has to do his round, where he gives each machine the same attention. With AIOT, the quality of the assets can be guarded with sensors. So, at his round, a foreman can give most attention to the machines which mostly need it.

The same applies to a sector as biomedical: ‘to prevent is better then to cure’. So, help your clients and/or yourself to stay healthy. An example is fall detection. And does the elderly take his medicine?

Help your patients with therapy, to make use of knowledge from all previous patients: is therapy going on track? Also: give the patients who need it the right amount of attention. Instead of seeing all your patients with a standard scheduled time-frame, and as a consequence, give none of them enough time really.  If therapy is lagging, you probably want to give those patient attentions. Is therapy going faster then expected: what are the reasons? How can this knowledge be used to improve therapy in the future? Besides, if people can do therapy and appointments at home, they don’t have to spend their precious time; where the actual time needed for treatment is shorter then the time spent on travelling and waiting.

Optimal circumstances

Sensors can guard that product are made or kept in optimal circumstances. E.g., if cutting parts of a machine are still sharp enough, and in their right precision. Or guard the temperature of cooling or keep an eye on the indoor air quality. This may also make guarantees possible, and thus creating added value to your products or service.

For public infrastructure, removing graffiti costs millions of euro each year. Besides the direct costs, there are the costs of not using the equipment and environmental costs. And naturally, the trains, buses, metro, etc. are your visit card to customers.

The thrill of success

Sometimes, graffiti can look beautiful. But mostly, it looks -and is- vandalism.

Non-removal is an invitation to even more graffiti. Tests in New York have turned out that the immediate removal of graffiti, at least the same day, discourages further graffiti. Besides, the subway of New York is guarded closely, so it has become difficult for vandals to create their painting.

To create something beautiful is mostly not the aim of the sprayers. Most painters do it for the ‘thrill’. The first thrill is to finish their work before they are noticed. The other is to see their work travelling the next day, knowing it will travel the whole country. There are solo-sprayers. But mostly, sprayers work in groups. Actions are being planned, to out-smarten the (railway) police.

Nowadays, public transport companies have guidelines when graffiti is noticed: an employer (e.g. the train manager notices the painting, signs a cleaning company and this company cleans the graffiti the same day with a mobile team.

But as the saying goes: prevention is better than curing! How can you diminish the change of graffiti paintings? Track & Trace solutions help.

Know if someone enters your shunting yard unwanted

The shunting yard is a known spot for graffiti painters. At night, or just on the day itself because it’s easy to enter the premises.

Most marshal yards are guarded by security. However, because they are quiet places, it is rather easy to enter the site and hide from security. Besides, most graffiti painters operate in groups. So, they are practiced to paint a wagon in no-time.

The Dutch regional TV broadcast OogTV: “Meanwhile I know, also from stories that I hear from colleagues from the country, that such artists are unstoppable. We can make the gates so high, and the locks so wide, but if these people want to, they will succeed.”

How Tracy can help: perimeter and object guard

Track and trace can help preventing graffiti in 2 ways:

  • Perimeter detection
  • Object guard

Know if someone enters the marshal yard or any other perimeter. Act immediately on ‘strange’ behavior, such as: unidentified persons on the premises. Or persons at times when you expect nobody will be there. Another option is to guard the object itself, such as a train wagon itself with a sensor. An alert is being send when persons approach the object.

So, you can prevent graffiti to take place or at least, to prevent the painter from finishing his ‘artwork’.

Find out how we can help you: https://www.advsolned.com/tracy-home/

Preventive Maintenance is one of the golden nuggets of IOT. How does this focus affect the deployment of personnel?

  • Efficiencty of personnel: more and better results
  • Challenge of scarcity of personnel
  • The challenges of the aging engineer

Efficiency of personnel: more and better results

There was and is a lot of attention what sensors can do for preventive maintenance: with preventive maintenance, huge costs of big repair costs are avoided by acting on time. One aspect in this way of thinking, was that existing personnel could work more efficiently. In old days, mechanics and engineers did their regular scheduled rounds of maintenance, where every device got similar time of attention, whether the device was in a bad state or not. Sensors measure the state of maintenance of devices real-time. As such, personnel can give attention to devices which really needs it. By using your existing personnel in this more efficient way, high personnel costs are saved because no other personnel would have been hired.

Challenge of Scarcity of personnel

When Preventive Maintenance became popular some years ago as one of the fields of Internet of Things, the world was still in the last phase of the economic crisis. Industry has in some ways still crisis thought: yes, personnel is hard to find. But they don’t make the connection that efficiency has changed in the guise of ‘cost saver’ to ‘benefit most from opportunities’. Because personnel is so hard to find, industry has to use the available personnel as efficient and effectively as possible. Besides, engineering for infrastructure isn’t a popular study any longer. So, engineers are even harder to find.

With preventive maintenance with the aid of sensors, personnel can give attention to the devices which really needs them.

The challenges of the aging engineer

There is more: most infrastructure has been built 20 years ago. Already, there’s the challenge that those engineers have moved on to other jobs. So, it’s very possible indeed that in a company, nobody knows how this infrastructure works exactly any longer. Last years, a new challenge has come up: those engineers are beginning to retire. That means that a pool of this specific knowledge is already decreasing and will even lessen more in the years to come. Therefore, it is very important to have measures for maintenance in place, before this knowledge has disappeared completely.

Benefits:

  • A longer lifetime for your equipment with Preventive maintenance
  • Create the future. Better serve your client, with solutions which weren’t possible until now!
  • More satisfied customers
  • More control on your processes
  • Better Security

One of the most important areas for IoT is Preventive Maintenance. With the modern solutions, you can measure if assets are working properly. And if not, you can repair or replace them, even before those assets have created damage. Examples are:

  • Are the industrial motors running properly?
  • Is the oil pressure and quality still ok?
  • Are there any glitches in the electrical wiring?
  • How can I save on energy?

With IoT, you can give your equipment a longer lifetime and thus save on repair and replacement costs. Besides, you can spare on costs because you have grip on your processes. For instance: more efficiency on energy costs, better results through optimal deployment of employees

Your customers will become more satisfied with your services. With solutions which weren’t possible until now, products can ‘think’ for their users. In IOT, users raise the expectations and will be dissatisfied with devices which do not help them.

A dashboard helps you to view in one glance which assets are working properly and which are probably in need of repair or replacement. Further, you learn when, where and how intensely your assets are being used, so you use your assets more efficiently.

In a world of connected devices, security is very important. Hackers will try to break in: to steal, to cause harm or to shut down your devices. Without security, hackers can make their entry from anywhere: from one of your devices, but also an unsecured device from one of your employee’s at home. So, in the world of IOT, security of these devices is key.

IoT solutions

IoT solutions prevent accidents from happening and reduce the response time for maintenance. As results, your costs of maintenance will be lower and equipment will have a longer lifetime through Preventive Maintenance.

Sensor measurement solutions look for deviations in normal use. So, you can act upon the first deviations and before the device isn’t working at all. Examples are:

  • Monitoring the health of an industrial motor
  • Monitoring oil quality in chain mechanisms
  • Smart metering for saving energy

Clean sensor data required for sensor fusion and accurate decision making

Sensor data (audio, pressure, temperature, weight, etc.) have to be measured. However, most sensor signals are disturbed by:

  • Powerline interference and glitches.
  • Environmental factors (including: dust and other contaminants).

ASN Consultancy is the modern way of working of algorithm design to separate the wanted sensor signals from the undesirable unwanted signals. So, you can analyze and take action on clean and accurate sensor data.

Dashboard

Our tailormade dashboard solutions provide you all the information you need at one glance. So, you act on devices which are not working properly anymore. You can see the use of each device and can even predict the use in time, based on your history data. With this information, you can gain more efficiency or you can improve the satisfaction of your customers.

Security

With a world where everything is connected, security is very important. Because of its importance, its size and the results of an eventual disruption, infrastructure is an important target for terrorist and (future) enemy governments.

Do you want to learn more: https://www.advsolned.com/asn-condition-monitoring/

Container thefts are increasingly common. “What should you do with such a thing?” headlines the newspaper article. Recently, the police found a number of containers that were once stolen. Tracy, the IOT track and trace device, can help you.

Why should someone want to steal a container?

So, why should someone want to steal a container? For an outsider, it might sound a bit strange. Customers see the container mostly as a kind of large, metal ‘box’ to dispose waste. For a container company, the hiring of the container means trust in your logistic solutions. But for a thief, a container means an easy to steal loot: it’s already packed and stands ready to pick-up!

Stealing is that simple: the scrap metal booty is already packed!

Stealing containers with scrap metal is especially popular. That does not have to mean that a container actually contains scrap metal or is completely full: the thief’s hope for loot is enough. Stealing a container is pretty simple: all the thief needs is a truck. He can put the container on the back with a cable or grab arm in no time. This theft means a major loss for companies: a container can easily cost 5 to 10 thousand euros, beside the eventual value of the cargo. And possibly the trust the customer has in you.

All that most companies do untill now is to share on social media camera images of their container or the truck that was stolen. Hoping to find the thief. Or at least to prevent a recurrence.

Tracy IoT helps: track and trace

• Perimeter detection

• Track and trace on container: Immediate theft signal

Perimeter detection

Tracy checks whether persons enter the site. When “strange” people enter the site, a signal is immediately triggered. Besides, Tracy monitors the movement of people and assets within the perimeter. Tracy uses Ultra Wide Band (UWB). One of the big advantages of UWB is its accuracy, so you know immediately where to look.

Track and trace on container: Immediate theft signal

When there are movements around a container, a signal goes off. If these are “strange”, for example at late times when nobody should be present, you can take immediate action. If the container is taken along anyway, it can be detected by the UWB signal.

Read more: https://www.advsolned.com/tracker-assets/

Competition on costs is ever increasing. Meanwhile, customers are more demanding in their expectations. In 2024, global smart sensor market will have a value of $80 billion. In others words: become part of the future or become obsolete!

Challenges Asset Managers

Asset managers are faced with the following challenges:

  • Asset managers demand huge cost savings
  • Tightening of budgets for maintenance programmes
  • Less service disruptions and customer complaints
  • Increasing demands from users
  • No Control and optimal use of my assets
  • Risk of hacking by terrorists
  • Remote firmware updates

With IoT, you can give your equipment a longer lifetime and thus save on repair and replacement costs.

Your customers will become more satisfied with your services. With solutions which weren’t possible until now, products can ‘think’ for their users. Like: the health of the lamp and power quality of street lights, refrigerators which will signal to a car that owner is out of milk, a space on a parking lot is reserved for the visitor when he’s close to the office etcetera.

And the other way around: remember the first time you went in a hotel which had Wi-Fi and you thought: “great”! You’ve probably forgotten; nowadays, not having Wi-Fi has since long became a standard. In IOT, users raise the expectations and will be dissatisfied with devices which do not help them.

A dashboard helps you to view in one glance which assets are working properly and which are probably in need of repair or replacement. Further, you learn when, where and how intensely your assets are being used, so you use your assets more efficiently.

In a world of connected devices, security is very important. Hackers will try to break in: to steal, to cause harm or to shut down your devices. Without security, hackers can make their entry from anywhere: from one of your devices, but also an unsecured device from one of your employee’s at home. So, in the world of IOT, security of these devices is key.

Read about solutions: https://www.advsolned.com/asn-condition-monitoring/

Working at water is a struggle with the elements. What are the challenges for ports? And how can IOT help to create smart ports?

Challenges for ports:

  • High maintenance costs
  • Measurement problems because of:
    • Interference
    • Non-communicating system
    • Security
    • Dust, heat, fog, ice,…
  • No control, no efficiency
  • Security of legacy systems

Working at water is a struggle with the elements: water, wind, dust, heat, pressure. So, you want to know if:

  • pipelines are going to leak before they are actually leaking.
  • That cables are beginning to wear out. That the oil level is still on the right level.
  • That you can act when dust or smear are blocking lenses.

With IoT, you can predict and prevent equipment failure by monitoring product wear and replacement rates.  As such, you improve the reliability of your assets and reduce downtime. And if you recognize little faults, you can solve them easily before they have become big and expensive problems.

Systems don’t communicate

Besides, most applications in a port environment are working, but do not communicate with each other. With our IoT solutions, you can monitor and control all your processes at the same time in 1 dashboard.

Optimize your just-in-time management

Meanwhile, you can optimize your just-in-time management as well. In the coming years, water transport will increase. On the other hand, the possibilities of a port to largen are most of times limited. To deal with the increasing pressure op ports and thus stay in a healthy competition, water transport needs to optimize the use of their equipment as efficient as possible. Like which ship can enter the port, the just-in-time allocation of ships to cranes, where a truck is already waiting to carry the load of the ship elsewhere.

Secure your assets and your load

Security has long time being disregarded, but is becoming one of the more important issues in Smart Water. And with reason: think about hacks on harbor terminals.